technocracy

Concrete is the most widely used construction material worldwide; however, reliable prediction of compressive strength remains challenging due to material heterogeneity, variable mix proportions, and sensitivity to field and environmental conditions. Recent advances in artificial intelligence enable data-driven modeling frameworks capable of supporting automated decision-making in construction quality control. This study leverages an industry-scale dataset consisting of approximately 70,000 compressive strength test records to evaluate and compare multiple predictive approaches, including linear regression, decision trees, random forests, transformer-based […]

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In most enterprise Databricks environments (like in MSC or large analytics ecosystems), system tables such as system.jobrunlogs or system.cluster_events may be restricted or disabled due to security or governance policies. However, tracking cluster utilization and cost is crucial for : Understanding how efficiently jobs use compute Identifying idle clusters or cost leaks Forecasting infrastructure budget Building custom cost dashboards This blog demonstrates a step-by-step approach to compute cluster utilization and cost using only Databricks REST APIs — no […]

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Thomas Berry challenges us to rethink humanity’s story on Earth: we are not masters of a mechanical world but participants in a sacred community of life. Framing ecological collapse as a spiritual crisis, he invites us to embrace a new cosmology of meaning, belonging, and partnership with the planet, and to reclaim our role as the Earth’s “consciousness.”

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Semantic associations such as the link between "bird" and "flew" are foundational for language modeling as they enable models to go beyond memorization and instead generalize and generate coherent text. Understanding how these associations are learned and represented in language models is essential for connecting deep learning with linguistic theory and developing a mechanistic foundation for large language models. In this work, we analyze how these associations emerge from natural language data in attention-based language models through the […]

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arXiv:2601.14259v1 Announce Type: new Abstract: Emotion recognition is a fundamental component of next-generation human-computer interaction (HCI), enabling machines to perceive, understand, and respond to users’ affective states. However, existing systems often rely on single-modality analysis such as facial expressions, speech tone, or textual sentiment, resulting in limited robustness and poor generalization in real-world environments. To address these challenges, this study proposes a Cloud-Based Cross-Modal Transformer (CMT) framework for multimodal emotion recognition and adaptive human-computer interaction. The proposed model […]

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Hsu et al. (2009) recently proposed an efficient, accurate spectral learning algorithm for Hidden Markov Models (HMMs). In this paper we relax their assumptions and prove a tighter finite-sample error bound for the case of Reduced-Rank HMMs, i.e., HMMs with low-rank transition matrices. Since rank-$k$ RR-HMMs are a larger class of models than $k$-state HMMs while being equally efficient to work with, this relaxation greatly increases the learning algorithm’s scope. In addition, we generalize the algorithm and bounds […]

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arXiv:2602.12405v1 Announce Type: new Abstract: Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or assume access to ample human annotations. Failures in the real world are typically subtle, combinatorial, and difficult to enumerate, whereas rich reasoning labels are expensive to acquire. We address this problem by introducing ARMOR: Adaptive Round-based Multi-task mOdel for Robotic failure detection and reasoning. We formulate detection and reasoning […]

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Whether you are speaking to corporate managers, Silicon Valley script kiddies, or seasoned academics pitching commercial applications of their research, you’re likely to hear a lot of claims about what AI is going to do. Hysterical discussions about AI machine learning’s applicability begin with a breathless recap of breakthroughs in predictive modeling (9X.XX% accuracy on ImageNet!, 5.XX% word error rate on speech recognition!) and then abruptly leap to prophesies of miraculous technologies that AI will drive in the […]

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